Text Classification
Transformers
Safetensors
English
Indonesian
bert
fill-mask
token-classification
cybersecurity
named-entity-recognition
Instructions to use codechrl/bert-base-cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codechrl/bert-base-cybersecurity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="codechrl/bert-base-cybersecurity")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codechrl/bert-base-cybersecurity") model = AutoModelForMaskedLM.from_pretrained("codechrl/bert-base-cybersecurity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from codechrl/bert-base-cybersecurity: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/codechrl/bert-base-cybersecurity/resolve/main/training_args.bin
- Command line
-
hf download hf://codechrl/bert-base-cybersecurity/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/codechrl/bert-base-cybersecurity/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- bfea11a733224eb0a90ee81646ab5bc0ce84e61df69ba4edcbed36df77748d9d
- Size of remote file:
- 5.84 kB
- SHA256:
- c11a953943d18125021ec6b039671808647923abd57ac008164b1365ae5cfcec
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